Department of Pharmacology and Physiology, Autism and Neurodevelopmental Disorders Institute, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.
Curr Opin Neurol. 2018 Apr;31(2):140-148. doi: 10.1097/WCO.0000000000000536.
Resting-state fMRI assessment of instrinsic functional brain connectivity (rs-fcMRI) in autism spectrum disorders (ASD) allows assessment of participants with a wide range of functioning levels, and collection of multisite databases that facilitate large-scale analysis. These heterogeneous multisite data present both promise and methodological challenge. Herein, we provide an overview of recent (1 October 2016-1 November 2017) empirical research on ASD rs-fcMRI, focusing on work that helps clarify how best to leverage the power of these data.
Recent research indicates that larger samples, careful atlas selection, and attention to eye status of participants will improve the sensitivity and power of resting-state fMRI analyses conducted using multisite data. Use of bandpass filters that extend into a slightly higher frequency range than typical defaults may prevent loss of disease-relevant information. Connectivity-based parcellation as an approach to region of interest analyses may allow for improved understanding of functional connectivity disruptions in ASD. Treatment approaches using rs-fcMRI to determine target engagement, predict treatment, or facilitate neurofeedback demonstrate promise.
Rs-fcMRI data have great promise for biomarker identification and treatment development in ASD; however, ongoing methodological development and evaluation is crucial for progress.
静息态功能磁共振成像评估自闭症谱系障碍（ASD）的固有功能脑连接（rs-fcMRI）可评估具有广泛功能水平的参与者，并可收集多站点数据库，从而促进大规模分析。这些异质的多站点数据既带来了希望，也带来了方法学上的挑战。在此，我们对最近（2016 年 10 月 1 日至 2017 年 11 月 1 日）关于 ASD rs-fcMRI 的实证研究进行概述，重点介绍有助于阐明如何最好地利用这些数据的研究。
最近的研究表明，更大的样本量、仔细的图谱选择以及关注参与者的眼睛状况将提高使用多站点数据进行静息态 fMRI 分析的灵敏度和功效。使用带通滤波器，将其扩展到比典型默认值稍高的频率范围，可能会防止丢失与疾病相关的信息。基于连接的分割作为一种感兴趣区域分析的方法，可以更好地理解 ASD 中的功能连接中断。使用 rs-fcMRI 进行治疗方法，以确定目标参与、预测治疗或促进神经反馈，都显示出了前景。
rs-fcMRI 数据在 ASD 的生物标志物识别和治疗开发方面具有很大的潜力；然而，持续的方法学开发和评估对于取得进展至关重要。
